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Abstract

Mesoscopic fluorescence molecular tomography (MFMT) is a novel imaging technique that aims at obtaining the 3-D distribution of molecular probes inside biological tissues at depths of a few millimeters. To achieve high resolution, around 100-150μm scale in turbid samples, dense spatial sampling strategies are required. However, a large number of optodes leads to sizable forward and inverse problems that can be challenging to compute efficiently. In this work, we propose a two-step data reduction strategy to accelerate the inverse problem and improve robustness. First, data selection is performed via signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) criteria. Then principal component analysis (PCA) is applied to further reduce the size of the sensitivity matrix. We perform numerical simulations and phantom experiments to validate the effectiveness of the proposed strategy. In both in silico and in vitro cases, we are able to significantly improve the quality of MFMT reconstructions while reducing the computation times by close to a factor of two.

Figures (8)

Optical schematic diagram of the 2nd Generation MFMT system. The de-scanned excitation (Ex) light and 2D detector array (EMCCD) compose the system backbone. Polarizing beam splitter (PBS) and cross-polarizers (P, A) minimize specular reflection from the sample surface along with the fluorescence filter (F). Scan lens (SL) and a tube lens form a conjugate image plane and 4F relay system forms the final image on the EMCCD. In higher binning configurations, the spatial integration of the photons deteriorates the dynamic range so a reflection block (RB) is introduced into the system. One set of images is completed after completing a raster scan.

The numerical phantom designed to mimic vascular structure with a main bio-printed vascular channel and sprouting capillaries. The main trunk has a diameter of 400μm and the off-shoot branches are 200μm in diameter and separated with one voxel spacing to test the resolution of the proposed method. (a), (b) and (c) are the full view, xy view, and xz view of the phantom, respectively. (d) and (e) show the SNR and CNR levels of the synthetic measurements.

Reconstruction results and evaluation metrics under random sampling. (a) plots the average metrics of reconstructions with different numbers of remaining measurements. (b) and (c) show two visual reconstruction results with 5,000 and 30,000 measurements left, respectively.

Reconstruction results and evaluation metrics under SNR-based data reduction strategy. (a) gives the distribution of the 48 detectors’ SNR level in a case scenario. (b) plots the computation time and measurements left corresponding to different threshold of SNR. (c) plots the 4 metrics versus different threshold of SNR. (d)-(f) show 3 visual reconstructions with retained measurements after filtering by the specific SNR threshold of 1.99, 2.02, and 2.05, respectively.

Reconstruction results and evaluation metrics under CNR-based data reduction strategy. (a) gives the distribution of the 48 detectors’ CNR level in a case scenario. (b) plots the computation time and measurements left corresponding to different thresholds of CNR. (c) plots the 4 metrics versus different thresholds of CNR. (d)-(f) show 3 visual reconstructions with retained measurements after filtering by the specific CNR threshold of 6.5, 7.5, and 10, respectively.

Reconstruction results and evaluation metrics under PCA-based data reduction strategy in a simulation. (a) gives the relationship between variance explained and different principal components in a simulation. (b) plots the computation time and 4 metrics versus measurements left corresponding to different thresholds of CPV. (c)-(g) show 5 visual reconstructions with retained measurements after filtering by the specific CPV threshold of 67.4, 81.2, 90.7, 95.8, and 97.5, respectively.

Phantom reconstruction under the proposed redundant data reduction method. (a) segmented micro-MRI slice and reconstruction across x-y plane. (b) segmented micro-MRI slice and reconstuction across y-z plane. (c) 3D overlaid image of micro-MRI and optimal reconstruction. (d) reconstruction result using full data. (e)reconstruction result based on the remaining data after noise suppression. (f) reconstruction results using the retained data after both noise suppression and PCA processing. (g) and (h) are the distributions of SNR and CNR of 48 detectors.